
Example: One Standard Deviation Above The Mean
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In a standard normal distribution, this value becomes Z = 0 + 1 = 1 (the mean of zero plus the standard deviation of 1).Ī data point one standard deviation above the mean is the 84.1 st percentile, which we can see in a standard normal table with z = 1.0. One Standard Deviation Above The Meanįor a data point that is one standard deviation above the mean, we get a value of X = M + S (the mean of M plus the standard deviation of S). So a value of 260 in the normal distribution is equivalent to a z-score of 1.5 in a standard normal distribution.

For example, given the data point X = 260 in the original normal distribution, we get the following Z-value in the standard normal distribution: We can find a specific value of Z for any given value of X. Then Z has a mean of 0 and a standard deviation of 1 (a standard normal distribution). Then, we divide every data point by the standard deviation (S = 40). To convert to a standard normal distribution, we subtract the mean (M = 200) from every data point. Let’s say we have a normal distribution with mean M = 200 and standard deviation S = 40. Example: Converting A Normal Distribution To A Standard Normal Distribution Where X is the variable for the original normal distribution and Z is the variable for the standard normal distribution. This leaves the mean at 0, but changes the standard deviation from S to 1. Then, we divide every data point by the standard deviation S of the distribution. (You can learn more about when the mean increases or decreases here). This changes the mean from M to 0, but leaves the standard deviation unchanged.

To do this, we first subtract the value of the mean M of the distribution from every data point. However, we first need to convert the data to a standard normal distribution, with a mean of 0 and a standard deviation of 1. We can use a standard normal table to find the percentile rank for any data value from a normal distribution. A standard normal distribution has a mean of 0 and a standard deviation of 1.
